The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …

Although neural networks have seen tremendous success as predictive models in a variety
of domains, they can be overly confident in their predictions on out-of-distribution (OOD) …

Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the
highly dynamic, partially observable environment. Occlusions are highly prevalent in such …

Navigating complex and dynamic environments requires autonomous vehicles (AVs) to
reason about both visible and occluded regions. This involves predicting the future motion of …

Safety and performance are key enablers for autonomous driving: on the one hand we want
our autonomous vehicles (AVs) to be safe, while at the same time their performance (eg …

As the pretraining technique is growing in popularity, little work has been done on pretrained
learning-based motion prediction methods in autonomous driving. In this paper, we propose …

Achieving fully autonomous driving requires not only understanding the current surrounding
conditions but also predicting how objects that could lead to potential risks may change in …

Environment prediction frameworks are critical for the safe navigation of autonomous
vehicles (AVs) in dynamic settings. LiDAR-generated occupancy grid maps (L-OGMs) offer a …

Reasoning with occluded traffic agents is a significant open challenge for planning for
autonomous vehicles. Recent deep learning models have shown impressive results for …

Automated Vehicles (AVs) have been receiving increasing attention as a potential highly
mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to …